Topic
Cepstrum
About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.
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09 Jan 2014
TL;DR: The proposed work presents automatic classification of Indian Classical instruments based on spectral and MFCC features using well trained back propogation neural network classifier using Principal Component Analysis.
Abstract: In applications such as music information and database retrieval systems, classification of musical instruments plays an important role. The proposed work presents automatic classification of Indian Classical instruments based on spectral and MFCC features using well trained back propogation neural network classifier. Musical instruments such as Harmonium, Santo or and Tabla are considered for an experimentation. The spectral features such as amplitude and spectral range along with Mel Frequency Cepstrum Coefficients are considered as features. Being features are not distinguished, classification is done using non parametric classifiers such as neural networks. Being number of cepstrum coefficients are large important coefficients are selected using Principal Component Analysis. It has been observed that using 42 samples for training and 18 for testing, back propogation neural network provides accuracy of 98%. The present work can be extended for more number of Hindustani and Carnitic classical musical Instruments.
14 citations
TL;DR: In this paper, the differential cepstrum was introduced to design equiripple minimum phase FIR filters by using cepstral deconvolution; this fast procedure only takes three FFT computation and avoids the complicated phase wrapping and polynomial root-finding algorithms.
Abstract: The differential cepstrum is introduced to design equiripple minimum phase FIR filters by using cepstral deconvolution; this fast procedure only takes three FFT computation and avoids the complicated phase wrapping and polynomial root-finding algorithms.
14 citations
01 Jan 2014
TL;DR: In this article, the use of the cepstrum for removing components from a signal which manifest themselves as periodic spectral components has been described, including discrete frequency components with uniform spacing such as families of harmonics and modulation sidebands, but also narrow band noise peaks coming from slight random modulation of almost periodic signals.
Abstract: The use of the cepstrum for removing components from a signal which manifest themselves as periodic spectral components has previously been described. These include discrete frequency components with uniform spacing such as families of harmonics and modulation sidebands, but also narrow band noise peaks coming from slight random modulation of almost periodic signals, such as higher harmonics of blade pass frequencies. The removal is effected by applying a notch “lifter” to the real cepstrum of the signal, thus removing the targeted components from the log amplitude spectrum, and then combining the modified amplitude spectrum with the original phase spectrum. Not much attention was previously paid to the type of notch lifter, but two different situations occurring in conjunction with analysis of signals from wind turbines showed that different lifters have advantages in different situations. This chapter describes two different approaches, illustrating them with the two examples of application.
14 citations
23 Aug 2004
TL;DR: A new approach to two-dimensional (2D) blind deconvolution of ultrasonic images with stable results of clearly higher spatial resolution and better defined tissue structures than in the input images.
Abstract: The paper presents a new approach to two-dimensional (2D) blind deconvolution of ultrasonic images. Homomorphic deconvolution, so far the most successful method in this field, is based on the assumption that the point spread function (PSF) and the tissue signal lie in different bands of the cepstrum domain, which is not completely true. Furthermore, 2D phase unwrapping is necessary in 2D homomorphic mapping, which is an ill-posed and noise-sensitive problem. Here both limitations are avoided using blind iterative deconvolution namely Van Cittert algorithm with reblurring. Simplified homomorphic deconvolution is used only for initial estimation. The algorithm is applied to the whole radiofrequency image, meaning that only the global spatially invariant component of the PSF is removed. Tests on synthetic and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images.
14 citations
15 Apr 2018
TL;DR: In a subjective comparison category rating test, the proposed ABE solution significantly outperforms the competing ABE baseline and was found to improve NB speech quality by 0.80 CMOS points, while the computation time is reduced to about 3 % compared to the ABE baseline.
Abstract: In this work, we present a simple deep neural network (DNN)-based regression approach to artificial speech bandwidth extension (ABE) in the frequency domain for estimating missing speech components in the range 4 … 7 kHz The upper band (UB) spectral magnitudes are found by first estimating the UB cepstrum by means of a DNN regression and subsequent conversion to the spectral domain, leading to a more efficient and generalizing model training rather than estimating highly redundant UB magnitudes directly As second novelty the phase information for the estimated upper band spectral magnitudes is generated by spectrally shifting the NB phase Apart from framing, this very simple approach does not introduce additional algorithmic delay A cross-database and cross-language task is defined for training and evaluation of the ABE framework In a subjective comparison category rating test, the proposed ABE solution significantly outperforms the competing ABE baseline and was found to improve NB speech quality by 080 CMOS points, while the computation time is reduced to about 3 % compared to the ABE baseline
14 citations